GRIN: Generative Relation and Intention Network for Multi-agent Trajectory Prediction

Longyuan Li, Jian Yao, Li Kevin Wenliang, Tong He, Tianjun Xiao, Junchi Yan, David Wipf, Zheng Zhang

Keywords: Trajectory prediction, variational Autoencoder, deep learning, probabilistic

TL;DR: We build a model that disentangles social relations and human intents to generate multimodal future trajectories of multi-agent systems.

Abstract: Learning the distribution of future trajectories conditioned on the past is a crucial problem for understanding multi-agent systems. This is challenging because humans make decisions based on complex social relations and personal intents, resulting in highly complex uncertainties over trajectories. To address this problem, we propose a conditional deep generative model that combines advances in graph neural networks. The prior and recognition model encodes two types of latent codes for each agent: an inter-agent latent code to represent social relations and an intra-agent latent code to represent agent intentions. The decoder is carefully devised to leverage the codes in a disentangled way to predict multi-modal future trajectory distribution. Specifically, a graph attention network built upon inter-agent latent code is used to learn continuous pair-wise relations, and an agent's motion is controlled by its latent intents and its observations of all other agents. Through experiments on both synthetic and real-world datasets, we show that our model outperforms previous work in multiple performance metrics. We also show that our model generates realistic multi-modal trajectories.

Code Of Conduct: I certify that all co-authors of this work have read and commit to adhering to the NeurIPS Statement on Ethics, Fairness, Inclusivity, and Code of Conduct.

Code: https://github.com/longyuanli/GRIN_NeurIPS21